Metrics question
Developers commonly complete authentication setup but drop off before receiving their first successful webhook event. How would you redesign the onboarding flow end to end, from credentials and local testing to sample code, webhook verification, retries, and debugging, and what metrics would you use to determine whether the new flow actually improved developer success?
- Glean
- Metrics
- Medium
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
What this question tests
Tests redesigning a developer onboarding funnel end to end and defining metrics that prove the redesign actually reduced drop-off, not just moved it.
How to approach it
- Map the funnel precisely: credentials setup, local testing, first sample-code run, webhook registration, webhook verification, and first successful event received.
- Diagnose where drop-off concentrates today, likely between webhook registration and verification, since that step depends on external network configuration developers often get wrong.
- Redesign that step specifically: a built-in webhook tester that sends a synthetic event and shows the developer exactly what was received and why it failed.
- Improve sample code to include working retry and debugging logic by default, not just a happy-path example.
- Add inline debugging output, showing the last delivery attempt's status code and payload directly in the dashboard.
- Measure success with stage-by-stage conversion, time from signup to first successful webhook event, and support-ticket rate for webhook issues before and after.
What a strong answer includes
- Localizes the drop-off to a specific funnel stage, webhook verification, rather than redesigning the whole flow blindly.
- Proposes a concrete fix, a built-in webhook tester with synthetic events, tailored to the actual failure mode.
- Uses stage-by-stage conversion and ticket-rate change as before-and-after proof, not just overall activation rate.
Common mistakes
- Redesigning the entire onboarding flow without first localizing where developers actually drop off.
- Measuring only top-line activation without stage-level conversion data.
Likely follow-up questions
- How would you test the new webhook tester before rolling it out broadly?
- What would you do if drop-off persists even after the redesign?
More metrics questions
- What metrics prove Glean is delivering value to a large enterprise?Glean · Metrics · Hard
- After launching new agent security and governance features, how would you measure whether they are actually working for enterprise customers? Define a concise metric set that captures security outcomes, admin confidence, and end-user adoption, and explain which are leading vs. lagging indicators.Glean · Metrics · Hard
- You launch new governance and privacy features in Glean Protect. What metrics would you use to determine whether they are actually reducing enterprise AI risk and increasing customer trust, without hurting search/assistant adoption or answer usefulness? Include leading and lagging indicators, and explain how you’d avoid vanity metrics.Glean · Metrics · Hard
- Glean cares about time-to-first-call, integration success rate, and API error rates. Which metrics would you treat as the core indicators that external developers are actually reaching production successfully, which are just supporting diagnostics, and how would you instrument the platform to measure the funnel from initial setup to a live production integration?Glean · Metrics · Medium
- Glean wants customers to safely compare multiple LLMs before committing one to production. What end-user workflow and admin/API capabilities would you prioritize in v1, what would you leave out, and how would you measure whether the experimentation experience is actually helping customers make better rollout decisions?Glean · Metrics · Hard
- You own projections of LLM usage, cost, and capacity planning for a new LLM-native capability. How would you forecast demand at launch, monitor leading indicators after release, and decide when to secure more provider capacity versus routing traffic to alternative models?Glean · Metrics · Hard
More questions from Glean
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop